Porosity Prediction from Well Logs Using Back Propagation Neural Network Optimized by Genetic Algorithm in One Heterogeneous Oil Reservoirs of Ordos Basin, China

A reliable and effective model for reservoir physical property prediction is a key to reservoir characterization and management. At present, using well logging data to estimate reservoir physical parameters is an important means for reservoir evaluation. Based on the characteristics of large quantity and complexity of estimating process, we have attempted to design a nonlinear back propagation neural network model optimized by genetic algorithm (BPNNGA) for reservoir porosity prediction. This model is with the advantages of self-learning and self-adaption of back propagation neural network (BPNN), structural parameters optimizing and global searching optimal solution of genetic algorithm (GA). The model is applied to the Chang 8 oil group tight sandstone of Yanchang Formation in southwestern Ordos Basin. According to the correlations between well logging data and measured core porosity data, 5 well logging curves (gamma ray, deep induction, density, acoustic, and compensated neutron) are selected as the input neurons while the measured core porosity is selected as the output neurons. The number of hidden layer neurons is defined as 20 by the method of multiple calibrating optimizations. Modeling results demonstrate that the average relative error of the model output is 10.77%, indicating the excellent predicting effect of the model. The predicting results of the model are compared with the predicting results of conventional multivariate stepwise regression algorithm, and BPNN model. The average relative errors of the above models are 12.83%, 12.9%, and 13.47%, respectively. Results show that the predicting results of the BPNNGA model are more accurate than that of the other two, and BPNNGA is a more applicable method to estimate the reservoir porosity parameters in the study area.